Search results for: canopy characters classification

Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

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Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition

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Authors: M. Devaki, K. B. Jayanthi

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The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

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Authors: Nassib Abdallah, Pierre Chauvet, Abd El Salam Hajjar, Bassam Daya

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In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

Keywords: brain-computer interface, speech recognition, artificial neural network, electroencephalography, EEG, wernicke area

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Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

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In this paper, we propose an approach of detecting the behavior of the viewers of a TV program in a non-controlled environment. The experiment we propose is based on the use of three types of connected objects (smartphone, smart watch, and a connected remote control). 23 participants were observed while watching their TV programs during three phases: before, during and after watching a TV program. Their behaviors were detected using an approach based on The Dempster Shafer Theory (DST) in two phases. The first phase is to approximate dynamically the mass functions using an approach based on the correlation coefficient. The second phase is to calculate the approximate mass functions. To approximate the mass functions, two approaches have been tested: the first approach was to divide each features data space into cells; each one has a specific probability distribution over the behaviors. The probability distributions were computed statistically (estimated by empirical distribution). The second approach was to predict the TV-viewing behaviors through the use of classifiers algorithms and add uncertainty to the prediction based on the uncertainty of the model. Results showed that mixing the fusion rule with the computation of the initial approximate mass functions using a classifier led to an overall of 96%, 95% and 96% success rate for the first, second and third TV-viewing phase respectively. The results were also compared to those found in the literature. This study aims to anticipate certain actions in order to maintain the attention of TV viewers towards the proposed TV programs with usual connected objects, taking into account the various uncertainties that can be generated.

Keywords: Iot, TV-viewing behaviors identification, automatic classification, unconstrained environment

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Authors: Helen Frialde-Dupale

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Critical thinking Index (CTI) of 150 third year college students from five State Colleges and Universities (SUCs) in Region I were determined. Only students with Grade Point Average (GPA) of at least 2.0 from four general classification of degree courses, namely: Education, Arts and Sciences, Engineering and Agriculture were included. Specific problem No.1 dealt with the profile variables, namely: age, sex, degree course, monthly family income, number of siblings, high school graduated from, grade point average, personality type, highest educational attainment of parents, and occupation of parents. Problem No. 2 determined the critical thinking index among the respondents. Problem No. 3 investigated whether or not there are significant differences in the critical thinking index among the respondents across the profile variables. While problem No.4 determined whether or not there are significant relationship between the critical thinking index and selected profile variables, namely: age, monthly family income, number of siblings, and grade point average of the respondents. Finally, on problem No. 5, the critical thinking instrument which obtained the lowest rates, were used as basis for outlining an intervention program for enhancing critical thinking index (CTI) of students. The following null hypotheses were tested at 0.05 level of significance: there are no significant differences in the critical thinking index of the third college students across the profile variables; there are no significant relationships between the critical thinking index of the respondents and selected variables, namely: age, monthly family income, number of siblings, and grade point average.

Keywords: attitude as critical thinker, critical thinking applied, critical thinking index, self-perception as critical thinker

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Authors: Xiaoqing Wang, Junfeng Wang, Xiaolan Zhu

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Android operating system has been recognized by most application developers because of its good open-source and compatibility, which enriches the categories of applications greatly. However, it has become the target of malware attackers due to the lack of strict security supervision mechanisms, which leads to the rapid growth of malware, thus bringing serious safety hazards to users. Therefore, it is critical to detect Android malware effectively. Generally, the permissions declared in the AndroidManifest.xml can reflect the function and behavior of the application to a large extent. Since current Android system has not any restrictions to the number of permissions that an application can request, developers tend to apply more than actually needed permissions in order to ensure the successful running of the application, which results in the abuse of permissions. However, some traditional detection methods only consider the requested permissions and ignore whether it is actually used, which leads to incorrect identification of some malwares. Therefore, a machine learning detection method based on the actually used permissions combination and API calls was put forward in this paper. Meanwhile, several experiments are conducted to evaluate our methodology. The result shows that it can detect unknown malware effectively with higher true positive rate and accuracy while maintaining a low false positive rate. Consequently, the AdaboostM1 (J48) classification algorithm based on information gain feature selection algorithm has the best detection result, which can achieve an accuracy of 99.8%, a true positive rate of 99.6% and a lowest false positive rate of 0.

Keywords: android, API Calls, machine learning, permissions combination

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Authors: Valentina Adduci

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The developing research aims to define in general the criteria of environmental sustainability of rural buildings in Puglia and particularly in the manor farm. The main part of the study analyzes the relationship / dependence between the rural building and the landscape which, after many stratifications, results clearly identified and sometimes also characterized in a positive way. The location of the manor farm, in fact, is often conditioned by the infrastructural network and by the structure of the agricultural landscape. The manor farm, without the constraints due to the urban pattern’s density, was developed in accordance with a logical settlement that gives priority to the environmental aspects. These vernacular architectures are the most valuable example of how our ancestors have planned their dwellings according to nature. The 237 farms, analysis’ object, have been reported in cartography through the GIS system; a symbol has been assigned to each of them to identify the architectural typology and a different color for the historical period of construction. A datasheet template has been drawn up, and it has made possible a deeper understanding of each manor farm. This method provides a faster comparison of the most recurring characters in all the considered buildings, except for those farms which benefited from special geographical conditions, such as proximity to the road network or waterways. Below there are some of the most frequently constants derived from the statistical study of the examined buildings: southeast orientation of the main facade; placement of the sheep pen on the ground tilted and exposed to the south side; larger windowed surface on the south elevation; smaller windowed surface on the north elevation; presence of shielding vegetation near the more exposed elevations to the solar radiation; food storage’s rooms located on the ground floor or in the basement; animal shelter located in north side of the farm; presence of tanks and wells, sometimes combined with a very accurate channeling storm water system; thick layers of masonry walls, inside of which were often obtained hollow spaces to house stairwells or depots for the food storage; exclusive use of local building materials. The research aims to trace the ancient use of bioclimatic constructive techniques in the Apulian rural architecture and to define those that derive from an empirical knowledge and those that respond to an already encoded design. These constructive expedients are especially useful to obtain an effective passive cooling, to promote the natural ventilation and to built ingenious systems for the recovery and the preservation of rainwater and are still found in some of the manor farms analyzed, most of them are, today, in a serious state of neglect.

Keywords: bioclimatic devices, farmstead, rural landscape, sustainability

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Authors: Pukhtoon Yar

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Peshawar City is experiencing a rapid spatial urban growth primarily as a result of high rate of urbanization along with economic development. This paper was designed to understand the impacts of urbanization on agriculture land use change by particularly focusing on land use change trajectories from the past (1991-2014). We used Landsat imageries (30 meters) for1991along with Spot images (2.5 meters) for year 2014. . The ground truthing of the satellite data was performed by collecting information from Peshawar Development Authority, revenue department, real estate agents and interviews with the officials of city administration. The temporal satellite images were processed by applying supervised maximum likelihood classification technique in ArcGIS 9.3. The procedure resulted into five main classes of land use i.e. built-up area, farmland, barren land, cultivable-wasteland and water bodies. The analysis revealed that, in Peshawar City the built-up environment has been doubled from 8.1 percent in 1991 to over 18.2 percent in 2014 by predominantly encroaching land producing food. Furthermore, the CA-Markov Model predicted that the area under impervious surfaces would continue to flourish during the next three decades. This rapid increase in built-up area is accredited to the lack of proper land use planning and management, which has caused chaotic urban sprawl with detrimental social and environmental consequences.

Keywords: Urban Expansion, Land use, GIS, Remote Sensing, Markov Model, Peshawar City

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: Justin Lee, Siraj Shaikh, Alice Chu MD

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Children with cerebral palsy (CP) commonly present with upper extremity impairment, affecting one or both extremities, and are classified using the Manual Ability Classification Scale (MACS). The MACS defines bimanual hand abilities for children ages 4-18 years in everyday tasks and is a gradient scale, with I being nearly normal and V requiring total assistance. Children with more severe upper extremity impairment (MACS III-V) are often underrepresented, and relatively few effective therapies have been identified for these patients. Current orthoses are static and are only meant to prevent the progression of contractures in these patients. Other limitations include cost, comfort, accessibility, and longevity of the orthoses. Taking advantage of advances in 3D printing technology, we have created a highly customizable upper extremity orthotic that can be produced at a low cost. Iterations in our design have resulted in an orthotic that is custom fit to the patient based on scans of their arm, made of rigid polymer when needed to provide support, flexible material where appropriate to allow for comfort, and designed with a mechanical pulley system to allow for some functional use of the arm while in the orthotic. Preliminary data has shown that our orthotic can be built at a fraction of the cost of current orthoses and provide clinically significant improvement in assisting hand assessment (AHA) and pediatric quality of life scores (PedsQL).

Keywords: upper extremity orthosis, upper extremity, orthosis, 3-D printing, cerebral palsy, occupational therapy, spasticity, customizable

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Authors: Dasom Kim, Chen Liu, Myungsu Lim, Su-Hyeon Jeon, ByeoungKug Jeon, Kee-Young Kwahk, Namgyu Kim

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Recently, numerous documents including unstructured data and text have been created due to the rapid increase in the usage of social media and the Internet. Each document is usually provided with a specific category for the convenience of the users. In the past, the categorization was performed manually. However, in the case of manual categorization, not only can the accuracy of the categorization be not guaranteed but the categorization also requires a large amount of time and huge costs. Many studies have been conducted towards the automatic creation of categories to solve the limitations of manual categorization. Unfortunately, most of these methods cannot be applied to categorizing complex documents with multiple topics because the methods work by assuming that one document can be categorized into one category only. In order to overcome this limitation, some studies have attempted to categorize each document into multiple categories. However, they are also limited in that their learning process involves training using a multi-categorized document set. These methods therefore cannot be applied to multi-categorization of most documents unless multi-categorized training sets are provided. To overcome the limitation of the requirement of a multi-categorized training set by traditional multi-categorization algorithms, we previously proposed a new methodology that can extend a category of a single-categorized document to multiple categorizes by analyzing relationships among categories, topics, and documents. In this paper, we design a survey-based verification scenario for estimating the accuracy of our automatic categorization methodology.

Keywords: big data analysis, document classification, multi-category, text mining, topic analysis

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Authors: A. Wolfe

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Dayo Forster’s impressively crafted Reading the Ceiling (2007) enjoys a relatively high profile among Western readers. It is one of only a handful of Gambian novels to be published by an international publisher, Simon and Schuster of London, and was subsequently shortlisted for the Commonwealth Writer’s Best First Book Prize in 2008. It is currently available to US readers in print and as an e-book and has 167 ratings on Goodreads. This paper addresses the possible influence of the book on Western readers’ perception of The Gambia, or Africa in general, through its depiction of the conditions of Gambian women’s lives. Through a close reading of passages and analysis of imagery, intertextuality, and characterization in the book, the paper demonstrates that Forster portrays the culture of The Gambia as oppressively patriarchal and the prospects for young girls who stay in the country as extremely limited. Reading the Ceiling starts on Ayodele’s 18th birthday, the day she has planned to have sex for the first time. Most of the rest of the book is divided into three parts, each following the chain of events that occur after sex with a potential partner. Although Ayodele goes abroad for her education in each of the three scenarios, she ultimately capitulates to the patriarchal politics and demands of marriage and childrearing in The Gambia, settling for relationships with men she does not love, cooking and cleaning for husbands and children, and silencing her own opinions and desires in exchange for the familiar traditions of patriarchal—and, in one case, polygamous—marriage. Each scenario ends with resignation to death, as, after her mother’s funeral, Ayodele admits to herself that she will be next. Forster uses dust and mud imagery throughout the novel to indicate the dinginess of Ayodele’s life as a young woman, and then wife and mother, in The Gambia as well as the inescapability of this life. This depiction of earthen material is also present in the novel’s recounting of an oral tale about a mermaid captured by fishermen, a story that mirrors Ayodele’s ensnarement by traditional marriage customs and gender norms. A review of the fate of other characters in the novel reveals that Ayodele is not the only woman who becomes trapped by the expectations for women in The Gambia, as those who stay in the country end up subservient to their husbands and/or victims of men’s habitual infidelity. It is important to note that Reading the Ceiling is focused on the experiences of a minority—The Gambia’s middle class, Christian urban dwellers with money for education. Regardless of its limited scope, the novel clearly depicts The Gambia as a place where women are simply unable to successfully contend against traditional patriarchal norms. Although this novel evokes vivid imagery of The Gambia through original and compelling descriptions of food preparation, clothing, and scenery, it perhaps does little to challenge stereotypical perceptions of the lives of African women among a Western readership.

Keywords: African literature, commonwealth literature, marriage, stereotypes, women

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Authors: Pooja Ahuja, Tejal Bhutani, M. S. Dahiya

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Cheiloscopy, the study of lip prints, is a tool in forensic investigation technique that deals with identification of individuals based on lips patterns. The objective of this study is to determine predominant lip print pattern found among the Gujarati population, to evaluate whether any sex difference exists and to study the permanence of the pattern over six months duration. The study comprised of 100 healthy individuals (50 males and 50 females), in the age group of 18 to 25 years of Gujarati population of the Gandhinagar region of the Gujarat state, India. By using Suzuki and Tsuchihashi classification, Lip prints were then divided into four quadrants and also classified on the basis of peripheral shape of the lips. Materials used to record the lip prints were dark brown colored lipstick, cellophane tape, and white bond paper. Lipstick was applied uniformly, and lip prints were taken on the glued portion of cellophane tape and then stuck on to a white bond paper. These lip prints were analyzed with magnifying lens and virtually with stereo microscope. On the analysis of the subject population, results showed Branched pattern Type II (29.57 percentage) to be most predominant in the Gujarati population. Branched pattern Type II (35.60 percentage) and long vertical Type I (28.28 percentage) were most prevalent in males and females respectively and large full lips were most predominantly present in both the sexes. The study concludes that lip prints in any form can be an effective tool for identification of an individual in a closed or open group forms.

Keywords: cheiloscopy, lip pattern, predomianant, Gujarati population

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Authors: Xue Wang, Li-Wei Fan

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The technology innovation has become the driving force of social and economic development and transformation. The guidance and support of public policies is an important condition to promote the realization of technology innovation goals. Policy effectiveness evaluation is instructive in policy learning and adjustment. This paper reviews existing studies and systematically evaluates the effectiveness of policy-driven technological innovation. We used 167 articles from WOS and CNKI databases as samples to clarify the measurement of technological innovation indicators and analyze the classification and application of policy evaluation methods. In general, technology innovation input and technological output are the two main aspects of technological innovation index design, among which technological patents are the focus of research, the number of patents reflects the scale of technological innovation, and the quality of patents reflects the value of innovation from multiple aspects. As for policy evaluation methods, statistical analysis methods are applied to the formulation, selection and evaluation of the after-effect of policies to analyze the effect of policy implementation qualitatively and quantitatively. The bibliometric methods are mainly based on the public policy texts, discriminating the inter-government relationship and the multi-dimensional value of the policy. Decision analysis focuses on the establishment and measurement of the comprehensive evaluation index system of public policy. The economic analysis methods focus on the performance and output of technological innovation to test the policy effect. Finally, this paper puts forward the prospect of the future research direction.

Keywords: technology innovation, index, policy effectiveness, evaluation of policy, bibliometric analysis

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Authors: Goulet Marie-Pier, Voyer Dominic, Simoneau Victoria

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In a previous research project (2011-2019) funded by the Quebec Ministry of Education, an educational approach was developed based on the teaching and learning of place value through children's literature. Subsequently, the effect of this approach on the conceptual understanding of the concept among first graders (6-7 years old) was studied. The current project aims to create a series of children's literature to help older elementary school students (8-10 years old) in developing a conceptual understanding of complex mathematical concepts taught at their grade level rather than a more typical procedural understanding. Knowing that there are no educational material or children's books that exist to achieve our goals, four stories, accompanied by mathematical activities, will be created to support students, and their teachers, in the learning and teaching of mathematical concepts that can be challenging within their mathematic curriculum. The stories will also introduce a mathematical dialogue into the characters' discourse with the aim to address various mathematical foundations for which there are often erroneous statements among students and occasionally among teachers. In other words, the stories aim to empower students seeking a real understanding of difficult mathematical concepts, as well as teachers seeking a way to teach these difficult concepts in a way that goes beyond memorizing rules and procedures. In order to choose the concepts that will be part of the stories, it is essential to understand the current landscape regarding the main difficulties experienced by students in third and fourth grade (8-10 years old) and their teacher’s needs. From this perspective, the preliminary phase of the study, as discussed in the presentation, will provide critical insight into the mathematical concepts with which the target grade levels struggle the most. From this data, the research team will select the concepts and develop their stories in the second phase of the study. Two questions are preliminary to the implementation of our approach, namely (1) what mathematical concepts are considered the most “difficult to teach” by teachers in the third and fourth grades? and (2) according to teachers, what are the main difficulties encountered by their students in numeracy? Self-administered online questionnaires using the SimpleSondage software will be sent to all third and fourth-grade teachers in nine school service centers in the Quebec region, representing approximately 300 schools. The data that will be collected in the fall of 2022 will be used to compare the difficulties identified by the teachers with those prevalent in the scientific literature. Considering that this ensures consistency between the proposed approach and the true needs of the educational community, this preliminary phase is essential to the relevance of the rest of the project. It is also an essential first step in achieving the two ultimate goals of the research project, improving the learning of elementary school students in numeracy, and contributing to the professional development of elementary school teachers.

Keywords: children’s literature, conceptual understanding, elementary school, learning and teaching, mathematics

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Authors: S. Hutasavi, D. Chen

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The built-up area is a significant proxy to measure regional economic growth and reflects the Gross Provincial Product (GPP). However, an up-to-date and reliable database of built-up areas is not always available, especially in developing countries. The cloud-based geospatial analysis platform such as Google Earth Engine (GEE) provides an opportunity with accessibility and computational power for those countries to generate the built-up data. Therefore, this study aims to extract the built-up areas in Eastern Economic Corridor (EEC), Thailand using day and nighttime satellite imagery based on GEE facilities. The normalized indices were generated from Landsat 8 surface reflectance dataset, including Normalized Difference Built-up Index (NDBI), Built-up Index (BUI), and Modified Built-up Index (MBUI). These indices were applied to identify built-up areas in EEC. The result shows that MBUI performs better than BUI and NDBI, with the highest accuracy of 0.85 and Kappa of 0.82. Moreover, the overall accuracy of classification was improved from 79% to 90%, and error of total built-up area was decreased from 29% to 0.7%, after night-time light data from the Visible and Infrared Imaging Suite (VIIRS) Day Night Band (DNB). The results suggest that MBUI with night-time light imagery is appropriate for built-up area extraction and be utilize for further study of socioeconomic impacts of regional development policy over the EEC region.

Keywords: built-up area extraction, google earth engine, adaptive thresholding method, rapid mapping

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Authors: Arth J. Shah, Madhu R. Kamble

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Voice biometric liveness detection is customized to certify an authentication process of the voice data presented is genuine and not a recording or synthetic voice. With the rise of deepfakes and other equivalently sophisticated spoofing generation techniques, it’s becoming challenging to ensure that the person on the other end is a live speaker or not. Voice Liveness Detection (VLD) system is a group of security measures which detect and prevent voice spoofing attacks. Motivated by the recent development of the Kolmogorov-Arnold Network (KAN) based on the Kolmogorov-Arnold theorem, we proposed KAN for the VLD task. To date, multilayer perceptron (MLP) based classifiers have been used for the classification tasks. We aim to capture not only the compositional structure of the model but also to optimize the values of univariate functions. This study explains the mathematical as well as experimental analysis of KAN for VLD tasks, thereby opening a new perspective for scientists to work on speech and signal processing-based tasks. This study emerges as a combination of traditional signal processing tasks and new deep learning models, which further proved to be a better combination for VLD tasks. The experiments are performed on the POCO and ASVSpoof 2017 V2 database. We used Constant Q-transform, Mel, and short-time Fourier transform (STFT) based front-end features and used CNN, BiLSTM, and KAN as back-end classifiers. The best accuracy is 91.26 % on the POCO database using STFT features with the KAN classifier. In the ASVSpoof 2017 V2 database, the lowest EER we obtained was 26.42 %, using CQT features and KAN as a classifier.

Keywords: Kolmogorov Arnold networks, multilayer perceptron, pop noise, voice liveness detection

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Authors: Saad M. Darwish, Magda M. Madbouly, Murad B. Khorsheed

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Sign Languages (SL) are the most accomplished forms of gestural communication. Therefore, their automatic analysis is a real challenge, which is interestingly implied to their lexical and syntactic organization levels. Hidden Markov models (HMM’s) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal for visual recognition of complex, structured hand gestures such as are found in sign language. In this paper, several results concerning static hand gesture recognition using an algorithm based on Type-2 Fuzzy HMM (T2FHMM) are presented. The features used as observables in the training as well as in the recognition phases are based on Singular Value Decomposition (SVD). SVD is an extension of Eigen decomposition to suit non-square matrices to reduce multi attribute hand gesture data to feature vectors. SVD optimally exposes the geometric structure of a matrix. In our approach, we replace the basic HMM arithmetic operators by some adequate Type-2 fuzzy operators that permits us to relax the additive constraint of probability measures. Therefore, T2FHMMs are able to handle both random and fuzzy uncertainties existing universally in the sequential data. Experimental results show that T2FHMMs can effectively handle noise and dialect uncertainties in hand signals besides a better classification performance than the classical HMMs. The recognition rate of the proposed system is 100% for uniform hand images and 86.21% for cluttered hand images.

Keywords: hand gesture recognition, hand detection, type-2 fuzzy logic, hidden Markov Model

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Authors: Ahmad Idris, O.A. Uche, Ado Y Abdulfatah

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Lateritic soils are found in abundance and are the most common types of soils used in construction of roads and embankments in Nigeria. Strength properties of the soils depend on the amount of compaction applied and the amount of water available in the soil at the time of compaction. In this study, the influence of the compactive effort and that of the amount of water in the soil in the determination of the shear strength properties of lateritic soil was investigated. Lateritic soil sample was collected from an existing borrow pit in Kano, Nigeria and its basic characteristics were determined and the soil was classified according to AASHTO classification method. The soil was then compacted under various compactive efforts and at wide range of moisture contents. The maximum dry density (MDD) and optimum moisture content (OMC) at each compactive effort was determined. Unconfined undrained triaxial test was carried out to determine the shear strength properties of the soil under various conditions of moisture and energy. Preliminary results obtained indicated that the soil is an A-7-5 soil. The final results obtained shows that as the compaction energy is increased, both the cohesion and friction angle increased irrespective of the moisture content used in the compaction. However, when the amount of water in the soil was increased and compaction effort kept constant, only the cohesion of the soil increases while the friction angle shows no any pattern of variation. It was also found that the highest values for cohesion and friction angle were obtained when the soil was compacted at the highest energy and at OMC.

Keywords: laterite, OMC, compaction energy, moisture content

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Authors: Nur Afiqah Mohd Kamal, Khamarrul Azahari Razak

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The earthquake aftermath has become a major concern, especially in high seismicity region. In Kundasang, Sabah, the earthquake on 5th June 2015 resulted in several catastrophes; landslides, rockfalls, mudflows and major slopes affected regardless of the series of the aftershocks. Certainly, the consequences of earthquake generate and induce the episodic disaster, not only life-threatening but it also affects infrastructure and economic development. Therefore, a need for investigating the change in land use and land cover (LULC) of post-earthquake geohazard is essential for identifying the extent of disastrous effects towards the development in Kundasang. With the advancement of remote sensing technology, post-earthquake geohazards (landslides, mudflows, rockfalls, debris flows) assessment can be evaluated by the employment of object-based image analysis in investigating the LULC change which consists of settlements, public infrastructure and vegetation cover. Therefore, this paper discusses the preliminary results on post-earthquakes geohazards distribution in Kundasang and evaluates the LULC classification effect upon the occurrences of geohazards event. The result of this preliminary analysis will provide an overview to determine the extent of geohazard impact on LULC. This research also provides beneficial input to the local authority in Kundasang about the risk of future structural development on the geohazard area.

Keywords: geohazard, land use land cover, object-based image analysis, remote sensing

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Authors: Maryam Amiri, Ali Rajabzade, Gholam Reza Goudarzi, Reza Heidari

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Growth of technology and environmental changes are so fast and therefore, companies and industries have much tendency to do activities of R&D for active participation in the market and achievement to a competitive advantages. Aviation industry and its subdivisions have high level technology and play a special role in economic and social development of countries. So, in the aviation industry for getting new technologies and competing with other countries aviation industry, there is a requirement for capability in R&D. Considering of appropriate R&D strategy is supportive that day technologies of the world can be achieved. Biofuel technology is one of the newest technologies that has allocated discussion of the world in aviation industry to itself. The purpose of this research has been formulation of R&D strategy of biofuel technology in aviation industry of Iran. After reviewing of the theoretical foundations of the methods and R&D strategies, finally we classified R&D strategies in four main categories as follows: internal R&D, collaboration R&D, out sourcing R&D and in-house R&D. After a review of R&D strategies, a model for formulation of R&D strategy with the aim of developing biofuel technology in aviation industry in Iran was offered. With regard to the requirements and aracteristics of industry and technology in the model, we presented an integrated approach to R&D. Based on the techniques of decision making and analyzing of structured expert opinion, 4 R&D strategies for different scenarios and with the aim of developing biofuel technology in aviation industry in Iran were recommended. In this research, based on the common features of the implementation process of R&D, a logical classification of these methods are presented as R&D strategies. Then, R&D strategies and their characteristics was developed according to the experts. In the end, we introduced a model to consider the role of aviation industry and biofuel technology in R&D strategies. And lastly, for conditions and various scenarios of the aviation industry, we have formulated a specific R&D strategy.

Keywords: aviation industry, biofuel technology, R&D, R&D strategy

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Authors: Michael A. Kraus, Navid Pourmoghaddam, Martin Botz, Jens Schneider, Geralt Siebert

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Fragmentation analysis of tempered glass gives insight into the quality of the tempering process and defines a certain degree of safety as well. Different standard such as the European EN 12150-1 or the American ASTM C 1048/CPSC 16 CFR 1201 define a minimum number of fragments required for soda-lime safety glass on the basis of fragmentation test results for classification. This work presents an approach for the glass breakage pattern prediction using a Voronoi Tesselation over Random Fields. The random Voronoi tessellation is trained with and validated against data from several breakage patterns. The fragments in observation areas of 50 mm x 50 mm were used for training and validation. All glass specimen used in this study were commercially available soda-lime glasses at three different thicknesses levels of 4 mm, 8 mm and 12 mm. The results of this work form a Bayesian framework for the training and prediction of breakage patterns of tempered soda-lime glass using a Voronoi Random Field Tesselation. Uncertainties occurring in this process can be well quantified, and several statistical measures of the pattern can be preservation with this method. Within this work it was found, that different Random Fields as basis for the Voronoi Tesselation lead to differently well fitted statistical properties of the glass breakage patterns. As the methodology is derived and kept general, the framework could be also applied to other random tesselations and crack pattern modelling purposes.

Keywords: glass breakage predicition, Voronoi Random Field Tessellation, fragmentation analysis, Bayesian parameter identification

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Authors: Ming Gao, Matthew Tingchi Liu, Hoi Ngan Loi

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Live streaming refers to the video contents generated by broadcasters and shared with viewers in real-time by uploading them to short-video platforms. In recent years, individual KOL broadcasters have successfully made use of live streams to sell a large amount of goods to the consumers. For example, Wei Ya, the Number 1 broadcaster in Taobao Live, sold products worth RMB 2.7 billion (USD 0.38 billion) in 2018. Regarding the success of live streaming commerce (LSC) in China, this study explores the elements of the booming LSC industry and attempts to explain the reasons behind its prosperity. A systematic review of industry reports and academic papers was conducted to summarize the latest findings in this field. And the results of this investigation showed that a live streaming eco-system has been established by the LSC players, namely, the platform, the broadcaster, the product supplier, and the viewer. In this eco-system, all players have complementary advantages and needs, and their close cooperation leads to a win-win situation. For instance, platforms and broadcasters have abundant internet traffic, which needs to be monetized, while product suppliers have mature supply chains and the need of promoting the products. In addition, viewers are attached to the LSC platforms to get product information, bargains, and entertainment. This study highlights the importance of the mass-personal hybrid communication nature of live streaming because its interpersonal communication feature increases consumers’ positive experiences, while its mass media broadcasting feature facilitates product promotion. Another innovative point of this study lies in its inclusion of the special characteristic of Chinese Internet culture - entertainment. The entertaining genres of the live streams created by broadcasters serve as down-to-earth approaches to reach their audiences easily. Further, the nature of video, i.e., the dynamic and salient stimulus, is emphasized in this study. Since video is more engaging, it can attract viewers in a quick and easy way. Meanwhile, the abundant, interesting, high-quality, and free short videos have added “stickiness” to platforms by retaining users and prolonging their staying time on the platforms. In addition, broadcasters’ important characters, such as physical attractiveness, humor, sex appeal, kindness, communication skills, and interactivity, are also identified as important factors that influence consumers’ engagement and purchase intention. In conclusion, all players have their own proper places in this live streaming eco-system, in which they work seamlessly to give full play to their respective advantages, with each player taking what it needs and offering what it has. This has contributed to the success of live streaming commerce in China.

Keywords: broadcasters, communication, entertainment, live streaming commerce, viewers

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Authors: Rattana Sangchan, Pattaraporn Thampradit

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Teaching students to use a variety of reading methods in developing reading is essential for Thai university students. However, there haven’t been a lot of studies concerned about developing reading methods that are used by Thai students in the industrial education field. Therefore, this study was carried out not only to investigate the developing reading methods of Industrial Education students at King Mongkut’s Institute of Technology Ladkrabang, but also to determine if the developing reading strategies differ among the students’ reading abilities and differ gender: male and female. The research instrument used in collecting the data consisted of fourteen statements which include either metacognitive strategies, cognitive strategies or social / affective strategies. Results of this study revealed that students could develop their reading methods in moderate level (mean=3.13). Furthermore, high reading ability students had different levels of using reading methods to develop their reading from those of mid reading ability students. In addition, high reading ability students could use either metacognitive reading methods or cognitive reading methods to develop their reading much better than mid reading ability students. Interestingly, male students could develop their reading methods in great levels while female students could develop their reading methods only in moderate level. Last but not least, male students could use either metacognitive reading methods or cognitive reading methods to develop their reading much better than female students. Thus, the results of this study could indicate that most students need to apply much more reading strategies to develop their reading. At the same time, suggestions on how to motivate and train their students to apply much more appropriate effective reading strategies to better comprehend their reading were also provided.

Keywords: developing reading methods, industrial education, reading abilities, reading method classification

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Authors: Yagahira E. Castro-Sesquen, Chloe Kim, Robert H. Gilman, David J. Sullivan, Peter C. Searson

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Diagnosis of severe malaria is particularly important in highly endemic regions since most patients are positive for parasitemia and treatment differs from non-severe malaria. Diagnosis can be challenging due to the prevalence of diseases with similar symptoms. Accurate diagnosis is increasingly important to avoid overprescribing antimalarial drugs, minimize drug resistance, and minimize costs. A nanoparticle-based assay for detection and quantification of Plasmodium falciparum histidine-rich protein 2 (HRP2) in urine and serum is reported. The assay uses magnetic beads conjugated with anti-HRP2 antibody for protein capture and concentration, and antibody-conjugated quantum dots for optical detection. Western Blot analysis demonstrated that magnetic beads allows the concentration of HRP2 protein in urine by 20-fold. The concentration effect was achieved because large volume of urine can be incubated with beads, and magnetic separation can be easily performed in minutes to isolate beads containing HRP2 protein. Magnetic beads and Quantum Dots 525 conjugated to anti-HRP2 antibodies allows the detection of low concentration of HRP2 protein (0.5 ng mL-1), and quantification in the range of 33 to 2,000 ng mL-1 corresponding to the range associated with non-severe to severe malaria. This assay can be easily adapted to a non-invasive point-of-care test for classification of severe malaria.

Keywords: HRP2 protein, malaria, magnetic beads, Quantum dots

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Authors: Goodness Onwuka, Khaled Abou-El-Hossein

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Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.

Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding

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Authors: Ling Yu Debbie Tsoi

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After China has just exceeded the USA as the top Hollywood film market in 2018, Hollywood studios have been adapting the taste, preference, casting and even film title translation to resonate with the Chinese audience. Due to the huge foreign demands, Hollywood film directors are paying closer attention to the translation of their products, as film titles are entry gates to the film and serve advertising, informative, aesthetic functions. Other than film directors and studios, comments over quality film title translation also appear on various online clip viewing platforms, online media, and magazines. In particular, netizens in mainland China, Hong Kong, and Taiwan seems to defend film titles in their own region while despising the other two regions. In view of the endless debates and lack of systematic analysis on film title translation in Greater China, the study aims at investigating the translation of Hollywood film titles (from English to Chinese) across Greater China based on Venuti’s (1991; 1995; 1998; 2001) concept of domestication and foreignization. To offer a comparison over time, a mini-corpus was built comprised of the top 70 most popular Hollywood film titles in 1987- 1988, 1997- 1998, 2007- 2008 and 2017- 2018 of Greater China respectively. Altogether, 560 source texts and 1680 target texts of mainland China, Hong Kong, and Taiwan were compared against each other. The three regions are found to have a distinctive style and patterns of translation. For instance, a sizable number of film titles are foreignized in mainland China by adopting literal translation and transliteration, whereas Hong Kong and Taiwan prefer domestication. Hong Kong tends to adopt a more vulgar style by using colloquial Cantonese slangs and even swear words, associating characters with negative connotations. Also, English is used as a form of domestication in Hong Kong from 1987 till 2018. Use of English as a strategy of domestication was never found in mainland nor Taiwan. On the contrary, Taiwanese target texts tend to adopt a cute and child-like style by using repetitive words and positive connotations. Even if English was used, it was used as foreignization. As film titles represent cultural products of popular culture, it is suspected that Hongkongers would like to develop cultural identity via adopting style distinctive from mainland China by vulgarization and negativity. Hongkongers also identify themselves as international cosmopolitan, leading to their identification with English. It is also suspected that due to former colonial rule of Japan, Taiwan adopts a popular culture similar to Japan, with cute and childlike expressions.

Keywords: cultural identification, ethnic identification, Greater China, film title translation

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Authors: S. Shankar Bharathi

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Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Vesna Bosilj Vukšić, Lucija Ivančić, Dalia Suša Vugec

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While struggling to succeed in today’s complex market environment and provide better customer experience and services, enterprises encompass digital transformation as a means for reaching competitiveness and foster value creation. A digital transformation process consists of information technology implementation projects, as well as organizational factors such as top management support, digital transformation strategy, and organizational changes. However, to the best of our knowledge, there is little evidence about digital transformation endeavors in organizations and how they perceive it – is it only about digital technologies adoption or a true organizational shift is needed? In order to address this issue and as the first step in our research project, a literature review is conducted. The analysis included case study papers from Scopus and Web of Science databases. The following attributes are considered for classification and analysis of papers: time component; country of case origin; case industry and; digital transformation concept comprehension, i.e. focus. Research showed that organizations – public, as well as private ones, are aware of change necessity and employ digital transformation projects. Also, the changes concerning digital transformation affect both manufacturing and service-based industries. Furthermore, we discovered that organizations understand that besides technologies implementation, organizational changes must also be adopted. However, with only 29 relevant papers identified, research positioned digital transformation as an unexplored and emerging phenomenon in information systems research. The scarcity of evidence-based papers calls for further examination of this topic on cases from practice.

Keywords: digital strategy, digital technologies, digital transformation, literature review

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